{"type":"ScholarlyArticle","title":"Scalable Speed-ups for the SMS-EMOA from a Simple Aging Strategy","authors":[{"name":"Mingfeng Li","slug":"mingfeng-li","given":"Mingfeng","family":"Li","affiliation":""},{"name":"Weijie Zheng","slug":"weijie-zheng","given":"Weijie","family":"Zheng","affiliation":""},{"name":"Benjamin Doerr","slug":"benjamin-doerr","given":"Benjamin","family":"Doerr","affiliation":""}],"abstract":"Different from single-objective evolutionary algorithms, where non-elitism is an established concept, multi-objective evolutionary algorithms almost always select the next population in a greedy fashion. In the only notable exception, a stochastic selection mechanism was recently proposed for the SMS-EMOA and was proven to speed up computing the Pareto front of the bi-objective jump benchmark with problem size n and gap parameter k by a factor of max{1,2^(k/4)/n}. While this constitutes the first proven speed-up from non-elitist selection, suggesting a very interesting research direction, it has to be noted that a true speed-up only occurs for k ≥ 4log(n), where the runtime is super-polynomial, and that the advantage reduces for larger numbers of objectives as shown in a later work. In this work, we propose a different non-elitist selection mechanism based on aging, which exempts individuals younger than a certain age from a possible removal. This remedies the two shortcomings of  stochastic selection: We prove a speed-up by a factor of max{1,Θ(k)^(k-1)}, regardless of the number of objectives. In particular,  a  positive speed-up can already be observed for constant k, the only setting for which polynomial runtimes can be witnessed. Overall, this result supports the use of non-elitist selection schemes, but suggests that aging-based mechanisms can be considerably more powerful than stochastic selection mechanisms.","doi":"10.24963/ijcai.2025/988","url":"https://ijcai.org/proceedings/2025/988","pdf":"https://ijcai.org/proceedings/2025/0988.pdf","markdown":"https://ijcai.org/proceedings/2025/988.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/988","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Search","track":"Main Track","number":988,"pages":{"first":8885,"last":8893},"keywords":["Search: S: Evolutionary computation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}